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I Made an API for Persistent KV-Caching (Cache Augmented Generation)

Hacker News

I Made an API for Persistent KV-Caching (Cache Augmented Generation)

Hi HN! I wanted to demonstrate an easy to use API for Cache Augment Generation. For any open source LLM available on Llama cpp, we can store the KV-cache and model state after it has processed a large corpus of documents, and then load that state in every time we query the document. This leads to a drastic reduction in latency as well as compute/energy used by the model. This demo is part of a larger system that I'm building called DataBridge[0] - with a focus on implementing new and useful techniques for knowledge retrieval - allowing developers to use the latest research in production. I'd love to hear your feedback on DataBridge, and the CAG feature. If you have papers or particular techniques you'd like to see implemented, I'd love to hear about it :) [0] https://github.com/databridge-org/databridge-core/

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, open · Missing: mac, agents, macos
92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, llama, io · Missing: https docs, excited, just released
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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